Using 250 observations for the money supply m, we estimate the followingregressions:Am -0.073m +0.315Am 10.270Am 21(0.032)(0.125)(0.072)0.194(0.14)Time left 0:24:02Values in (.) are the standard errors of the coefficient estimates. What is the order ofintegration of the money supply at the 1% significance level?Select one:a.At least (1)O b. None of the other options is trueOc. (1)Od. 1(2)Oe. (0)Clear my choiceNext pa

The objective of the question is to determine the order of integration of the money supply at the 1%…

Answered: Using 250 observations for the money…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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INSUR A life insurance company wishes to examine the relationship between the amount of life insurance held by a family and family income. From a random sample of households, the company collected the accompanying data. The data are in units of thousands of dollars. INCOME 97 38 141 29 y = x = Let INSUR 280 75 INCOME 303 81 453 137 The numerator of the slope coefficient formula for the estimated regression equation is: 357 77 a 400,201.10 199 43 b 392,354.02 251 53 384,660.80 807 184 d 377,118.44 147 45 272 70 537 128 527 117 245 55 483 116 673 204 194 46 154 51 163 48 280 69 507 140 464 136 321 71 873 206 476 144 574 111 251 65 497 130 826 171 133 32 259 82 281 73 446 146 332 77 219 48 208 55 180 48 169 42 273 69 502 127 547 126 281 80 428 143 370 77 221 49 214 51
Consider the following regression model and corresponding output for a dataset with n = 104 observations: y=ß₁+ß2x2+ß³¸*¸+4 3 4x4+u Variable β Std. Error t P>|t| X2 -0.012 0.006 -2.289 0.022 X3 0.596 0.014 41.139 0.000 X4 0.52 1.06 Constant 8.860 1.766 5.017 0.000 What is the marginal effect of x4 on y? (approximate at least to 3 decimal places)
1. Consider the following regression model y = x3 + u. (1) Let 3 denote the Ordinary Least Squares (OLS) estimator of B. The so-called Gauss- Markov assumptions are: • MLR.1: The true model in the population is given by (1). • MLR.2: We have a random sample of n observations {(ri, Yi), i = 1, 2, .., n} following the population model in (1). .... • MLR.3: No one explanatory variable can be written as a linear combination of the remaining explanatory variables that is, there is no perfectcollinearity. • MLR.4: In the population, the error u has an expected value of zero given any values of the explanatory variables, that is Elu|x] = 0. • MLR.5: In the population, the error u has the same variance given any values of the explanatory variables, that is Var[u|x] = o? , an unknown finite, positive constant. In the following scenarios, state whether B is an unbiased and consistent estimator of 3, and provide a brief justification for your answer in each case - but no formal mathematical…
Consider the following regression: Test Score, = 68.12 +2.52Hours Studied, - 0.04Hours Studied? %3D Without studying, an individual would average a test score of
We estimate the following regression: y = Bo + B1r1 + B2x2 + B3a3 +B44 +u using a sample of 85 observations. The regression yields the following: SSE = 227, SST = 650. %3D Given this information, compute the adjusted R-squared (R) for this regression. Round your answer to three decimals.
What are the various Standard errors in direct multiperiod regressions?
A. B. Consider data on births to women in the United States. Two variables of interest are the dependent variable, infant birth weight in ounces (bwght), and an explanatory variable, average number of cigarettes the mother smoked per day during pregnancy (cigs). The following simple regression was estimated using data on n = 1,388 births: bwght = 119.772 (0.572) n = 1,388, 0.514 cigs (0.091) R² = 0.0227, where standard errors are shown in parenthesis. What percent of the variation in birth weight is explained by cigs? What is the predicted birth weight when cigs = 0? What about when cigs = 20 (one pack per day)? Comment on the difference.
SSE SST X₂ The estimated regression equation for these data is ŷ = 7.6 +0.9x. Compute SSE, SST, and SSR (to 1 decimal). SSR 2 7 6 18 9 13 20 9 26 23 What percentage of the total sum of squares can be accounted for by the estimated regression equation (to 1 decimal)? % What is the value of the sample correlation coefficient (to 3 decimals)?
13. Collinearity in a multiple regression analysis Suppose you want to examine the effects of a training program on future earnings using the following model: earn98= 4.64 +2.376train +0.371earn96 +0.366educ- 1.86 age +2.534 married (1.14) (0.43) (0.016) (0.062) (0.013) (0.4) where earn 98- 1998 earnings, in thousands of dollars train -1 if the individual participated in the training program, and =0 otherwise earn 96- 1996 earnings, in thousands of dollars educ years of education age = age, in years married-1 if the individual is married, and -0 otherwise Suppose that there is a high degree of correlation (but not perfect) between earnings in 1996, education, age, and marital status. True or False: We should be concerned about this high degree of correlation because it affects our ability to reliably estimate the impact of the training program on 1998 earnings, T. True False
In multiple OLS regressions, if you are using power terms to fit for nonlinearity, how do you interpret the coefficients? For example: Yi=B1+B2X+B3X^2+Ui and B2 and B3 are both significant.
We have a pooled cross section of data with data from 2009 and 1999 on women’s wages and their years of education. We consider the model: wage = β0 + β1Year2009 + β2educ + β3Year2009 × educ + u where wage is the hourly wage and educ is the years of education. Year2009 is a dummy variable equal to 1 in the year 2009 and 0 otherwise. Which of the following measures the change in the effect of education on the wage between 1999 and 2009? a) β0 b) β1 + β2 + β3 c) none of these answers d) β1 + β3 e) β3
We are interested in understanding consumption of pork in the U.S. so we run a regression of annual per capita consumption of pork on a series of independent variables using data from 1990 to 2018 and obtain the following regression results (standard errors in parenthesis) CPt = -330.3 + 49.1 In Inct − 0.34 PPt + 0.33PBt (7.40) (0.13) (0.12) R²=0.71 DW=0.94 Where CPt is the annual per capita pounds of pork consumed in the U.S. in year t InInc, is the log of per capita disposable income in the U.S. in year t PP, is the average annualized real wholesale price of pork in the U.S. in year t (in cents per pound) PB, is the average annualized real wholesale price of beef in the U.S. in year t (in cents per pound) a. Interpret the partial slope coefficients. Does the sign on the coefficients agree or disagree with your a priori assumptions? Explain b. Using a two-sided test at the 5% significant level, determine if the partial slopes are statistically significant. c. Test the presence of…

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